from copy import deepcopy

import torch
from torch import nn, optim
import torch.nn.functional as F
from transformers import AdamW, BertConfig, BertForSequenceClassification

from model.scapt import SCAPT


class LabelSmoothLoss(nn.Module):
    def __init__(self, smoothing=0.0):
        super(LabelSmoothLoss, self).__init__()
        self.smoothing = smoothing

    def forward(self, input, target):
        log_prob = F.log_softmax(input, dim=-1)
        weight = input.new_ones(input.size()) * self.smoothing / (input.size(-1) - 1.)
        weight.scatter_(-1, target.unsqueeze(-1), (1. - self.smoothing))
        loss = (-weight * log_prob).sum(dim=-1).mean()
        return loss


class SupConLoss(nn.Module):
    """https://github.com/HobbitLong/SupContrast/blob/master/losses.py
    Supervised Contrastive Learning: https://arxiv.org/pdf/2004.11362.pdf.
    It also supports the unsupervised contrastive loss in SimCLR"""
    def __init__(self, temperature=0.07, contrast_mode='all',
                 base_temperature=0.07):
        super(SupConLoss, self).__init__()
        self.temperature = temperature
        self.contrast_mode = contrast_mode
        self.base_temperature = base_temperature

    def forward(self, features, labels=None, mask=None):
        """Compute loss for model. If both `labels` and `mask` are None,
        it degenerates to SimCLR unsupervised loss:
        https://arxiv.org/pdf/2002.05709.pdf
        Args:
            features: hidden vector of shape [bsz, n_views, ...].
            labels: ground truth of shape [bsz].
            mask: contrastive mask of shape [bsz, bsz], mask_{i,j}=1 if sample j
                has the same class as sample i. Can be asymmetric.
        Returns:
            A loss scalar.
        """
        device = (torch.device('cuda')
                  if features.is_cuda
                  else torch.device('cpu'))

        if len(features.shape) < 3:
            raise ValueError('`features` needs to be [bsz, n_views, ...],'
                             'at least 3 dimensions are required')
        if len(features.shape) > 3:
            features = features.view(features.shape[0], features.shape[1], -1)

        batch_size = features.shape[0]
        if labels is not None and mask is not None:
            raise ValueError('Cannot define both `labels` and `mask`')
        elif labels is None and mask is None:
            mask = torch.eye(batch_size, dtype=torch.float32).to(device)
        elif labels is not None:
            labels = labels.contiguous().view(-1, 1)
            if labels.shape[0] != batch_size:
                raise ValueError('Num of labels does not match num of features')
            mask = torch.eq(labels, labels.T).float().to(device)
        else:
            mask = mask.float().to(device)

        contrast_count = features.shape[1]
        contrast_feature = torch.cat(torch.unbind(features, dim=1), dim=0)
        if self.contrast_mode == 'one':
            anchor_feature = features[:, 0]
            anchor_count = 1
        elif self.contrast_mode == 'all':
            anchor_feature = contrast_feature
            anchor_count = contrast_count
        else:
            raise ValueError('Unknown mode: {}'.format(self.contrast_mode))

        # compute logits
        anchor_dot_contrast = torch.div(
            torch.matmul(anchor_feature, contrast_feature.T),
            self.temperature)
        # for numerical stability
        logits_max, _ = torch.max(anchor_dot_contrast, dim=1, keepdim=True)
        logits = anchor_dot_contrast - logits_max.detach()

        # tile mask
        mask = mask.repeat(anchor_count, contrast_count)
        # mask-out self-contrast cases
        logits_mask = torch.scatter(
            torch.ones_like(mask),
            1,
            torch.arange(batch_size * anchor_count).view(-1, 1).to(device),
            0
        )
        mask = mask * logits_mask

        # compute log_prob
        exp_logits = torch.exp(logits) * logits_mask
        log_prob = logits - torch.log(exp_logits.sum(1, keepdim=True) + 1e-30)

        # compute mean of log-likelihood over positive
        mean_log_prob_pos = (mask * log_prob).sum(1) / mask.sum(1)

        # loss
        loss = - (self.temperature / self.base_temperature) * mean_log_prob_pos
        loss = loss.view(anchor_count, batch_size).mean()

        return loss


def build_absa_model(config, embedding_layer=None):
    bert_config = BertConfig.from_pretrained("bert-base-uncased")
    bert_config.num_labels = 3
    bert_config.hidden_dropout_prob = config['dropout']
    bert_config.id2label = {
        0: 'positive',
        1: 'negative',
        2: 'neutral'
    }
    bert_config.label2id = {
        'positive': 0,
        'negative': 1,
        'neutral': 2,
    }
    bert_config.model = config['model']
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    if config['model'] == 'TransEnc':
        bert_config.num_attention_heads = config['head_size']
        bert_config.num_hidden_layers = config['layers']
        bert_config.hidden_size = config['hidden_size']
        bert_config.intermediate_size = config['feedforward']
        bert_for_facts_absa = SCAPT(bert_config, hidden_size=config['dense_hidden_size'])
    else:
        bert_for_facts_absa = SCAPT.from_pretrained('bert-base-uncased', config=bert_config).to(device)
    return bert_for_facts_absa


def build_optimizer(config, model):
    lr = config['learning_rate']
    weight_decay = config['weight_decay']
    opt = {
        'sgd': optim.SGD,
        'adam': optim.Adam,
        'adamw': AdamW,
        'adagrad': optim.Adagrad,
    }
    if 'momentum' in config:
        optimizer = opt[config['optimizer']](
            model.parameters(),
            lr=lr,
            weight_decay=weight_decay,
            momentum=config['momentum']
        )
    else:
        optimizer = opt[config['optimizer']](
            model.parameters(),
            lr=lr,
            weight_decay=weight_decay,
        )
    return optimizer
